Populate next row event in current row based on the event in Pandas dataframe

You can use two masks for finding vals and idxs and set values to index for result column.

mask1 = df['BrkPressState'] != df['BrkPressState'].shift()
vals = df.loc[mask1, 'VehSpdGS'][1:].values

mask2 = df['BrkPressState'] != df['BrkPressState'].shift(-1)
idxs = df.loc[mask2, 'VehSpdGS'][:-1].index

df.loc[idxs, 'result'] = vals
print(df)

    BrkPressState  VehSpdGS  result
0               1         2     NaN
1               1         3     NaN
2               1         2     NaN
3               1         4    12.0
4               0        12     NaN
5               0        13     NaN
6               0        11     3.0
7               1         3    15.0
8               0        15     NaN
9               0        14     NaN
10              0        15    12.0
11              1        12     NaN
12              1        13    14.0
13              0        14     NaN

Let us do diff to compare the previous and current row in BrkPressState column in order to identify boundaries, then mask and shift the values in VehSpdGS column

m = df['BrkPressState'].diff().ne(0)
df['Results'] = df['VehSpdGS'].mask(~m).shift(-1)

    BrkPressState  VehSpdGS  Results
0               1         2      NaN
1               1         3      NaN
2               1         2      NaN
3               1         4     12.0
4               0        12      NaN
5               0        13      NaN
6               0        11      3.0
7               1         3     15.0
8               0        15      NaN
9               0        14      NaN
10              0        15     12.0
11              1        12      NaN
12              1        13     14.0
13              0        14      NaN

You can also do shift(-1) on VehSpdGS and then replace values with NaN if df['BrkPressState'] != df['BrkPressState'].shift(-1)

Code:

df["result"]=df["VehSpdGS"].shift(-1).where(df['BrkPressState'] != df['BrkPressState'].shift(-1),pd.NA)
df

output: